The clinician educator career in oncology.
Bibliographic record
Abstract
11022 Background: There has been progress in both the definition of the work of a clinician educator (CE) and the skillset required. The CE career pathway has not been studied in oncology. Our aim is to study the current state of oncologists’ identification as a CE and their perceptions of the barriers and enablers for a CE career. Methods: A 27-item cross-sectional survey was completed by ASCO program directors (PDs) and associate/assistant PDs (APDs). The survey asked about their current career and perceptions about CE careers including barriers/enablers. Prior to distribution, the survey was reviewed by experts in oncology education and approved by the ASCO Education Council. Frequency statistics are presented. Results: Eighty-eight of 297 PDs/APDs responded (30%). 70 (80%) perceived CE as a viable career track, 48 (55%) had a CE track available to faculty at their institution and 72 (82%) considered themselves as a CE. Most PDs/APDs (59; 67%) reported no formal medical education training for their trainees and the majority (67; 76%) did not have a CE track for their fellows. While medical education responsibilities are perceived to be common amongst graduates (39% reporting >50% of graduates), 59 (67%) of PDs/APDs reported <10% of their trainees pursue medical education as a research focus. Compared to clinical, laboratory or discovery research, 71 (81%) of PDs/APDs felt their fellows were less or significantly less prepared for a career in education research. Table highlights the perceived barriers/enablers to a CE career. Conclusions: Many PDs/APDs perceive themselves as clinician educators. However, little to no formal education training currently exists to identify and nurture trainees into careers in education. Identification of training milestones in education and establishing guidelines for academic promotion for CEs in oncology are needed.[Table: see text]
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.066 | 0.010 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".